#004 - Import Data for Multiple Assets¶

On this code we import data from YFinance, export and read a CSV file, then perform some analysis and organize the DataFrame.

From #001;

Import libraries

  • Link to Pandas Documentation: https://pandas.pydata.org/docs/index.html

  • Link to datareader Documentation: https://pandas-datareader.readthedocs.io/en/latest/index.html

  • Link to Yf Documentation: https://pandas-datareader.readthedocs.io/en/latest/readers/yahoo.html

  • Link to Plotly Documentation: https://plotly.com/python/

In [1]:
#    !pip install pandas
#    !pip install pandas-datareader
#    !pip install numpy
#    !pip install yfinance
#    !pip install datetime
#    !pip install plotly_express
In [2]:
#import Libraries
import pandas as pd
from pandas_datareader import data as pdr
import numpy as np
import yfinance as yf
import datetime as dt
import plotly.express as px
import plotly.graph_objects as go

4.1 Define a portfolio, obtain and export data¶

In [3]:
# Code to obtain stock data using Pandas Datareader
# Select the start/end dates and ticker symbols
# Data is saved in a stock_data.csv file

tickers = [item for item in input("Enter the stock tickers, for portfolio (space them only) : ").split()]

file_name = "Portfolio.csv"

yf.pdr_override()

Enter the stock tickers, for portfolio (space them only) : AAPL AMZN GOOG META MSFT NVDA TSLA
In [4]:
# Define the start and end dates, last 5 years
end = dt.datetime.now()
start = end - dt.timedelta(days = 365*5)
In [5]:
#obtain data from Yahoo Finance
df = pdr.get_data_yahoo(tickers, start = start, end = end)
df

[*********************100%%**********************]  7 of 7 completed
Out[5]:
Adj Close Close ... Open Volume
AAPL AMZN GOOG META MSFT NVDA TSLA AAPL AMZN GOOG ... MSFT NVDA TSLA AAPL AMZN GOOG META MSFT NVDA TSLA
Date
2018-12-10 40.695984 82.051498 51.977501 141.850006 102.167152 37.670109 24.343332 42.400002 82.051498 51.977501 ... 104.800003 36.450001 24.000000 248104000 149896000 36154000 26422200 40801500 62947200 99202500
2018-12-11 40.463226 82.162003 52.587502 142.080002 103.116745 36.759731 24.450666 42.157501 82.162003 52.587502 ... 109.800003 38.889999 24.660667 189126800 124894000 27894000 20300300 42381900 67191200 94632000
2018-12-12 40.576008 83.177002 53.183998 144.500000 103.582054 36.935837 24.440001 42.275002 83.177002 53.183998 ... 110.889999 37.105000 24.628000 142510800 131960000 30476000 23696900 36183000 65413600 75405000
2018-12-13 41.019913 82.918999 53.095001 145.009995 103.933426 36.933376 25.119333 42.737499 82.918999 53.095001 ... 109.580002 37.697498 24.676666 127594400 105426000 26596000 18148600 31333400 47138400 110488500
2018-12-14 39.707371 79.595497 52.105000 144.059998 100.685776 36.328114 24.380667 41.369999 79.595497 52.105000 ... 108.250000 36.802502 25.000000 162814800 127344000 33732000 21785800 47043100 47182000 95064000
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2023-12-04 189.429993 144.839996 130.630005 320.019989 369.140015 455.059998 235.580002 189.429993 144.839996 130.630005 ... 369.100006 460.769989 235.750000 43389500 48294200 24117100 19037100 32063300 43754300 104099800
2023-12-05 193.419998 146.880005 132.389999 318.290009 372.519989 465.660004 238.720001 193.419998 146.880005 132.389999 ... 366.450012 454.660004 233.869995 66628400 46822400 19235100 16952100 23065000 37171800 137971100
2023-12-06 192.320007 144.520004 131.429993 317.450012 368.799988 455.029999 239.369995 192.320007 144.520004 131.429993 ... 373.540009 472.149994 242.919998 41089700 39679000 16360600 11294300 21182100 38059000 126436200
2023-12-07 194.270004 146.880005 138.449997 326.589996 370.950012 465.959991 242.639999 194.270004 146.880005 138.449997 ... 368.230011 457.000000 241.550003 47433900 52320500 38297500 15891500 23099800 35000100 106949500
2023-12-08 195.340103 147.270004 136.460999 330.920013 373.250000 476.250092 242.354996 195.340103 147.270004 136.460999 ... 369.200012 465.950012 240.270004 25404775 20995189 14086832 6687972 9610302 24494477 70541618

1259 rows × 42 columns

4.2 Manipulate and Organize Data¶

In [6]:
#select Ajusted Close Prices
Adj_Close_df = df['Adj Close']
Adj_Close_df
Out[6]:
AAPL AMZN GOOG META MSFT NVDA TSLA
Date
2018-12-10 40.695984 82.051498 51.977501 141.850006 102.167152 37.670109 24.343332
2018-12-11 40.463226 82.162003 52.587502 142.080002 103.116745 36.759731 24.450666
2018-12-12 40.576008 83.177002 53.183998 144.500000 103.582054 36.935837 24.440001
2018-12-13 41.019913 82.918999 53.095001 145.009995 103.933426 36.933376 25.119333
2018-12-14 39.707371 79.595497 52.105000 144.059998 100.685776 36.328114 24.380667
... ... ... ... ... ... ... ...
2023-12-04 189.429993 144.839996 130.630005 320.019989 369.140015 455.059998 235.580002
2023-12-05 193.419998 146.880005 132.389999 318.290009 372.519989 465.660004 238.720001
2023-12-06 192.320007 144.520004 131.429993 317.450012 368.799988 455.029999 239.369995
2023-12-07 194.270004 146.880005 138.449997 326.589996 370.950012 465.959991 242.639999
2023-12-08 195.340103 147.270004 136.460999 330.920013 373.250000 476.250092 242.354996

1259 rows × 7 columns

In [7]:
volume_df = df['Volume']
volume_df
Out[7]:
AAPL AMZN GOOG META MSFT NVDA TSLA
Date
2018-12-10 248104000 149896000 36154000 26422200 40801500 62947200 99202500
2018-12-11 189126800 124894000 27894000 20300300 42381900 67191200 94632000
2018-12-12 142510800 131960000 30476000 23696900 36183000 65413600 75405000
2018-12-13 127594400 105426000 26596000 18148600 31333400 47138400 110488500
2018-12-14 162814800 127344000 33732000 21785800 47043100 47182000 95064000
... ... ... ... ... ... ... ...
2023-12-04 43389500 48294200 24117100 19037100 32063300 43754300 104099800
2023-12-05 66628400 46822400 19235100 16952100 23065000 37171800 137971100
2023-12-06 41089700 39679000 16360600 11294300 21182100 38059000 126436200
2023-12-07 47433900 52320500 38297500 15891500 23099800 35000100 106949500
2023-12-08 25404775 20995189 14086832 6687972 9610302 24494477 70541618

1259 rows × 7 columns

In [8]:
#calculate percentage daily return
p_change_df = Adj_Close_df.pct_change() * 100
p_change_df.replace(np.nan, 0, inplace = True)
p_change_df
Out[8]:
AAPL AMZN GOOG META MSFT NVDA TSLA
Date
2018-12-10 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
2018-12-11 -0.571942 0.134677 1.173586 0.162140 0.929450 -2.416711 0.440918
2018-12-12 0.278726 1.235363 1.134293 1.703264 0.451245 0.479072 -0.043622
2018-12-13 1.094008 -0.310186 -0.167338 0.352937 0.339221 -0.006661 2.779594
2018-12-14 -3.199768 -4.008130 -1.864585 -0.655125 -3.124741 -1.638796 -2.940630
... ... ... ... ... ... ... ...
2023-12-04 -0.946461 -1.489494 -2.017703 -1.477747 -1.433872 -2.683629 -1.360801
2023-12-05 2.106322 1.408457 1.347313 -0.540585 0.915635 2.329365 1.332880
2023-12-06 -0.568706 -1.606754 -0.725135 -0.263909 -0.998604 -2.282782 0.272283
2023-12-07 1.013933 1.632992 5.341250 2.879188 0.582978 2.402038 1.366088
2023-12-08 0.550831 0.265522 -1.436619 1.325827 0.620026 2.208366 -0.117459

1259 rows × 7 columns

In [9]:
# Function to scale stock prices based on their initial starting price
# The objective of this function is to set all prices to start at a value of 1 
def price_scaling(raw_prices_df):
    scaled_prices_df = raw_prices_df.copy()
    for i in raw_prices_df.columns[0:]:
          scaled_prices_df[i] = raw_prices_df[i]/raw_prices_df[i][0]
    return scaled_prices_df
In [10]:
scaling_df = price_scaling(Adj_Close_df)
scaling_df
Out[10]:
AAPL AMZN GOOG META MSFT NVDA TSLA
Date
2018-12-10 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2018-12-11 0.994281 1.001347 1.011736 1.001621 1.009294 0.975833 1.004409
2018-12-12 0.997052 1.013717 1.023212 1.018682 1.013849 0.980508 1.003971
2018-12-13 1.007960 1.010573 1.021500 1.022277 1.017288 0.980443 1.031877
2018-12-14 0.975707 0.970068 1.002453 1.015580 0.985500 0.964375 1.001534
... ... ... ... ... ... ... ...
2023-12-04 4.654759 1.765233 2.513203 2.256045 3.613099 12.080135 9.677393
2023-12-05 4.752803 1.790095 2.547064 2.243849 3.646182 12.361525 9.806381
2023-12-06 4.725774 1.761333 2.528594 2.237927 3.609771 12.079339 9.833083
2023-12-07 4.773690 1.790095 2.663652 2.302362 3.630815 12.369489 9.967411
2023-12-08 4.799985 1.794848 2.625386 2.332887 3.653327 12.642652 9.955703

1259 rows × 7 columns

In [11]:
Adj_Close_df.describe().round(2)
Out[11]:
AAPL AMZN GOOG META MSFT NVDA TSLA
count 1259.00 1259.00 1259.00 1259.00 1259.00 1259.00 1259.00
mean 119.76 126.77 97.70 233.39 228.61 172.09 168.24
std 47.00 31.59 30.24 69.21 73.30 121.18 109.11
min 34.12 67.20 48.81 88.91 89.39 31.52 11.93
25% 72.54 95.05 68.01 179.78 161.48 65.50 45.52
50% 131.62 125.98 99.16 216.88 238.19 142.90 196.63
75% 156.31 158.09 125.50 294.28 286.49 226.40 250.87
max 195.93 186.57 150.71 382.18 382.70 504.05 409.97
In [12]:
p_change_df.describe().round(2)
Out[12]:
AAPL AMZN GOOG META MSFT NVDA TSLA
count 1259.00 1259.00 1259.00 1259.00 1259.00 1259.00 1259.00
mean 0.15 0.07 0.10 0.11 0.12 0.25 0.27
std 2.05 2.25 2.01 2.77 1.94 3.27 4.10
min -12.86 -14.05 -11.10 -26.39 -14.74 -18.45 -21.06
25% -0.83 -1.10 -0.88 -1.15 -0.85 -1.54 -1.78
50% 0.14 0.10 0.11 0.10 0.12 0.29 0.20
75% 1.25 1.22 1.10 1.43 1.12 1.99 2.22
max 11.98 13.54 10.45 23.28 14.22 24.37 19.89
In [13]:
scaling_df.describe().round(2)
Out[13]:
AAPL AMZN GOOG META MSFT NVDA TSLA
count 1259.00 1259.00 1259.00 1259.00 1259.00 1259.00 1259.00
mean 2.94 1.55 1.88 1.65 2.24 4.57 6.91
std 1.15 0.39 0.58 0.49 0.72 3.22 4.48
min 0.84 0.82 0.94 0.63 0.87 0.84 0.49
25% 1.78 1.16 1.31 1.27 1.58 1.74 1.87
50% 3.23 1.54 1.91 1.53 2.33 3.79 8.08
75% 3.84 1.93 2.41 2.07 2.80 6.01 10.31
max 4.81 2.27 2.90 2.69 3.75 13.38 16.84

4.3 Define a fucntion and plot Data¶

In [14]:
# Define a function using Plotly Express
def plotly_data(df, title):  
    
    # Create figure
    fig = go.Figure()
   
    # Set title
    fig.update_layout(title_text = title) 
    
    # For loop that plots all stock prices in the pandas dataframe df
    
    for i in df.columns[0:]:
        # Add range slider
        #fig.update_layout(xaxis=dict(rangeselector = dict(buttons=list([dict(count=1, label="1m", step="month", stepmode="backward"), dict(count=6, label="6m", step="month", stepmode="backward"), dict(count=1, label="YTD", step="year", stepmode="todate"), dict(count=1, label="1y", step="year", stepmode="backward"), dict(step="all")])), rangeslider=dict( visible=True), type="date"))
        # Add line graph
        fig.add_scatter(x = df.index, y = df[i], name = i)
        # Update Layout
        fig.update_layout({'plot_bgcolor': "white"})
    
    fig.show()
    
# Define a function using Plotly Express, changes axis y to logarithm scale
def log_plotly_data(df, title):  
    
    # Create figure
    fig = go.Figure()
   
    # Set title
    fig.update_layout(title_text = title) 
    
    # For loop that plots all stock prices in the pandas dataframe df
    
    for i in df.columns[0:]:
        # Add range slider
        #fig.update_layout(xaxis=dict(rangeselector = dict(buttons=list([dict(count=1, label="1m", step="month", stepmode="backward"), dict(count=6, label="6m", step="month", stepmode="backward"), dict(count=1, label="YTD", step="year", stepmode="todate"), dict(count=1, label="1y", step="year", stepmode="backward"), dict(step="all")])), rangeslider=dict( visible=True), type="date"))
        # Add line graph
        fig.add_scatter(x = df.index, y = df[i], name = i)
        # Update Layout
        fig.update_layout({'plot_bgcolor': "white"})
    
    #changes y to logarithm scale
    fig.update_yaxes(type="log")
    fig.show()
In [15]:
plotly_data(Adj_Close_df, 'Closing Prices [$]')
In [16]:
log_plotly_data(Adj_Close_df, 'Closing Prices [$]')
In [17]:
plotly_data(scaling_df, "Scaling Closing Prices")
In [18]:
log_plotly_data(scaling_df, "Scaling Closing Prices")
In [19]:
plotly_data(p_change_df, "Percentage Daily Returns [%]")
In [20]:
plotly_data(volume_df, "Trade Volume")